There are many uses for automated recognition of expressions of emotions, affective states, and similar psychological states. Perhaps the best known use is the smile detection in digital cameras. But there are others, including detection of facial reactions in response to various external stimuli, such as consumer reactions to advertisements, product displays, labeling, packaging, and pricing; and voter facial reactions to talking points and evaluation of debate performance. This list is far from exclusive.
It is desirable to automate recognition of expressions of emotions, affective states, and similar psychological states. Such automation reduces the costs of recognition, and also provides a measure of objectivity to the result of the recognition process. Automated (or machine) recognition of expressions of emotions, affective states, and similar psychological states is not a trivial design task, because facial expressions, poses, gestures and other face/body movements are not easy to define using standard programming techniques. Machine learning is a collection of techniques that may be used for this purpose. Machine learning allows artificial intelligence systems to learn from examples, in effect performing self-adaptation based on the training data. These techniques typically require large and carefully collected datasets of training examples, for example, a large number of sample images of different people, in different illumination conditions, of various ethnicities and different ages, and with a range of facial artifacts. The examples are needed to allow the machine classifier (recognizer) to discover the features that discriminate between different expression categories. While it may be relatively easy to collect examples of posed smiles from the Web, it is difficult to collect a large number of examples of real or realistic expressions of emotions such as fear, contempt, anger, disgust, and others.
A need exists for improved methods and apparatus for automatically detecting and classifying psychological states as exhibited by facial expressions, poses, gestures and other face/body movements, whether in reaction to known stimuli or otherwise. A need also exists for efficient methods to generate and collect data that can be used for training automatic classifiers of expressions of emotions, affective states, and similar psychological states.